An Effective Framework for Tweet Level Sentiment Classification using Recursive Text Pre-Processing Approach
Muhammad Bux Alvi, Ayesha Naeem, Abeer Mukhtiar, Muhammad Shakeel Akram · International Journal of Advanced Computer Science and Applications · 2019
With around 330 million people around the globe tweet 6000 times per second to express their feelings about a product, policy, service, or an event. Twitter message majorly consists of thoughts. Thoughts are mostly expressed as a text and it is an open challenge to extract some insight from free text. The scope of this work is to build an effective tweet level sentiment classification framework that may use these thoughts to know collective sentiment of the folk on a particular subject. Furthermore, this work also analyses the impact of proposed tweet level recursive text pre-processing approach on overall classification results. This work achieved up to 4 points accuracy improvement over baseline approach besides mitigating feature vector space.